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John Brazier - One of the best experts on this subject based on the ideXlab platform.
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Modelling Health State Valuation data
Oxford Medicine Online, 2020Co-Authors: John Brazier, Julie Ratcliffe, Joshua A. Salomon, Aki TsuchiyaAbstract:This chapter examines the technical issues in modelling Health State Valuation data. Most measures of Health define too many States to directly value all of them (e.g. SF-6D defines 18,000 Health States). The solution has been to value a subset and by using modelling to predict the values of all States. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine Health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of Health States for Valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.
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Design and analysis of Health State Valuation data for model-based economic eValuations and for economic eValuations alongside clinical trials
Oxford Medicine Online, 2020Co-Authors: John Brazier, Julie Ratcliffe, Joshua A. Salomon, Aki TsuchiyaAbstract:This chapter focuses upon the needs of two approaches, economic eValuations based on decision analytic models, and those alongside clinical trials in terms of the collection and analysis of Health State values. The first section of the chapter presents requirements that are likely to be common to any study in which Health State values are collected from patients and/or members of the general population, including: who to ask, mode of administration, timing of assessments, sample size, and handling uncertainty. The second section of the chapter considers issues specific to trial-based economic eValuations, and the final section considers issues specific to the design and analysis of Health State Valuation data for economic models.
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Health State values for the HUI 2 descriptive system: results from a UK survey
Health Economics, 2020Co-Authors: Christopher Mccabe, Jennifer Roberts, Katherine Stevens, John BrazierAbstract:This paper reports the results of a study to estimate a statistical Health State Valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble Health State preference data. A sample of 51 Health States were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting Health State Valuations for all 8000 States defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed Health State values within the Valuation sample and for a separate validation sample of Health States. However, there are concerns over large prediction errors for two Health States in the Valuation sample. These problems must be balanced against concerns over the validity of using the VAS based Health State Valuation data of the original HUI2 Valuation model. Copyright © 2004 John Wiley & Sons, Ltd.
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Health State values for the HUI 2 descriptive system: results from a UK survey
2020Co-Authors: Christopher Mccabe, Jennifer Roberts, Katherine Stevens, John BrazierAbstract:This paper reports the results of a study to estimate a statistical Health State Valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble Health State preference data. A sample of 51 Health States were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting Health State Valuations for all 8,000 States defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed Health State values within the Valuation sample and for a separate validation sample of Health States. However, there are concerns over large prediction errors for two Health States in the Valuation sample. These problems must be balanced against concerns over the validity of using the VAS based Health State Valuation data of the original HUI2 Valuation model.
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experience based utility and own Health State Valuation for a Health State classification system why and how to do it
European Journal of Health Economics, 2018Co-Authors: John Brazier, Aki Tsuchiya, Donna Rowen, Tessa Peasgood, Milad Karimi, Julie RatcliffeAbstract:In the estimation of population value sets for Health State classification systems such as the EuroQOL five dimensions questionnaire (EQ-5D), there is increasing interest in asking respondents to value their own Health State, sometimes referred to as “experience-based utility values” or, more correctly, own rather than hypothetical Health States. Own Health State values differ to hypothetical Health State values, and this may be attributable to many reasons. This paper critically examines whose values matter; why there is a difference between own and hypothetical values; how to measure own Health State values; and why to use own Health State values. Finally, the paper examines other ways that own Health State values can be taken into account, such as including the use of informed general population preferences that may better take into account experience-based values.
Paul Anthony Scuffham - One of the best experts on this subject based on the ideXlab platform.
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Study Protocol: Comparison of Inconsistency between Time Trade Off and Discrete Choice Experiments in EQ-5D-3 L Health State Valuations
2020Co-Authors: Sanjeewa Kularatna, Joshua Byrnes, Paul Anthony ScuffhamAbstract:Background: The EQ-5D-3L is the most widely used multi-attribute utility instrument for describing Health States. A popular method for valuing the EuroQOL five dimension (EQ-5D)-3L Health States is the time trade-off approach (TTO) where quality of life is traded against length of life. However, TTO Valuations can provide logically inconsistent values. That is, where a respondent provides a utility value for one Health State that is lower than the score they give for a logically worse Health State. More recently there has been a tendency by researchers to use discrete choice experiments (DCE) as opposed to TTO in Health State Valuation exercises; however, DCEs often exclude dominant choices by design. The aim of this paper is to explore the differences in the rate of logically inconsistency Health State Valuations between TTO and DCE methodologies. Methods: A representative sample of the Australian general population will be recruited from an online cohort. Of the 243 EQ-5D-3L Health States, a number of Health State sets, comprising of potentially logically inconsistent Health State pairs, will be used for the Valuation. Participants will be asked to value given Health State sets using both TTO and DCE methods. Consequently, the proposed study is not a Health State Valuation exercise, but rather an eValuation of competing methods under controlled circumstances. Logical inconsistency will be assessed based on comparing quantitative Health State Valuations within the TTO and Stated preferences from discrete choices within the DCE. The count of logical inconsistencies will be estimated at an individual level for both approaches and compared. The comparison of the two approaches will identify if there are significant differences between the number of logical inconsistencies produced from DCE and TTO methods.
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Health State Valuation in sri lanka using the eq 5d 3l precursor to qaly estimation in south asia
Value in Health, 2016Co-Authors: Sanjeewa Kularatna, Paul Anthony ScuffhamAbstract:Quality adjusted life years (QALYs) are used commonly as an outcome measure in Health economic eValuations. Health State Valuations are used to determine the country specific utility values which are necessary to derive QALYs. The objective of this study is to derive an algorithm to estimate utility values for the EQ-5D-3L Health States using preferences of Sri Lankan general population.
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Health State Valuation in low and middle income countries a systematic review of the literature
Value in Health, 2013Co-Authors: Sanjeewa Kularatna, Jennifer A Whitty, Newell Walter Johnson, Paul Anthony ScuffhamAbstract:Objective Cost-utility analysis is widely used in high-income countries to inform decisions on efficient Health care resource allocation. Cost-utility analysis uses the quality-adjusted life-year as the outcome measure of Health. High-income countries have undertaken Health State Valuation (HSV) studies to determine country-specific utility weights to facilitate Valuation of Health-related quality of life. Despite an evident need, however, the extent of HSVs in low- and middle-income countries (LMICs) is unclear. Methods The literature was searched systematically by using four databases and additional Web searches to identify HSV studies carried out in LMICs. The Preferred Reporting System for Systematic Reviews and Meta-Analysis (PRISMA) strategy was followed to ensure systematic selection of the articles. Results The review identified 17 HSV studies from LMICs. Twelve studies were undertaken in upper middle-income countries, while lower middle- and low-income countries contributed three and two studies, respectively. There were 7 generic HSV and 10 disease-specific HSV studies. The seven generic HSVs included five EuroQol five-dimensional questionnaire, one six-dimensional Health State short form (derived from short-form 36 Health survey), and one Assessment of Quality of Life Valuations. Time trade-off was the predominant Valuation method used across all studies. Conclusions This review found that Health State Valuations from LMICs are uncommon and utility weights are generally unavailable for these countries to carry out Health economic eValuation. More HSV studies need to be undertaken in LMICs to facilitate efficient resource allocation in their respective Health systems.
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study protocol for valuing eq 5d 3l and eortc 8d Health States in a representative population sample in sri lanka
Health and Quality of Life Outcomes, 2013Co-Authors: Sanjeewa Kularatna, Jennifer A Whitty, Newell Walter Johnson, Paul Anthony ScuffhamAbstract:Background: Economic eValuations to inform decisions about allocation of Health resources are scarce in Low and Middle Income Countries, including in Sri Lanka. This is in part due to a lack of country-specific utility weights, which are necessary to derive appropriate Quality Adjusted Life Years. The EQ-5D-3L, a generic multi-attribute instrument (MAUI), is most widely used to measure and value Health States in high income countries; nevertheless, the sensitivity of generic MAUIs has been criticised in some conditions such as cancer. This article describes a protocol to produce both a generic EQ-5D-3L and cancer specific EORTC-8D utility index in Sri Lanka. Method: EQ-5D-3L and EORTC-8D Health States will be valued using the Time Trade-Off technique, by a representative population sample (n = 780 invited) identified using stratified multi-stage cluster sampling with probability proportionate to size method. Households will be randomly selected within 30 clusters across four districts; one adult (≥18 years) within each household will be selected using the Kish grid method. Data will be collected via face-to-face interview, with a Time Trade-Off board employed as a visual aid. Of the 243 EQ-5D-3L and 81,290 EORTC-8D Health States, 196 and 84 respectively will be directly valued. In EQ-5D-3L, all Health States that combine level 3 on mobility with either level 1 on usual activities or self-care were excluded. Each participant will first complete the EQ-5D-3L, rank and value 14 EQ-5D-3L States (plus the worst Health State and “immediate death”), and then rank and value seven EORTC-8D States (plus “immediate death”). Participant demographic and Health characteristics will be also collected. Regression models will be fitted to estimate utility indices for EQ-5D-3L and EORTC-8D Health States for Sri Lanka. The dependent variable will be the utility value. Different specifications of independent variables will be derived from the ordinal EQ-5D-3L to test for the best-fitting model. Discussion: In Sri Lanka, a LMIC Health State Valuation will have to be carried out using face to face interview instead of online methods. The proposed study will provide the first country-specific Health State Valuations for Sri Lanka, and one of the first Valuations to be completed in a South Asian Country.
Sanjeewa Kularatna - One of the best experts on this subject based on the ideXlab platform.
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Study Protocol: Comparison of Inconsistency between Time Trade Off and Discrete Choice Experiments in EQ-5D-3 L Health State Valuations
2020Co-Authors: Sanjeewa Kularatna, Joshua Byrnes, Paul Anthony ScuffhamAbstract:Background: The EQ-5D-3L is the most widely used multi-attribute utility instrument for describing Health States. A popular method for valuing the EuroQOL five dimension (EQ-5D)-3L Health States is the time trade-off approach (TTO) where quality of life is traded against length of life. However, TTO Valuations can provide logically inconsistent values. That is, where a respondent provides a utility value for one Health State that is lower than the score they give for a logically worse Health State. More recently there has been a tendency by researchers to use discrete choice experiments (DCE) as opposed to TTO in Health State Valuation exercises; however, DCEs often exclude dominant choices by design. The aim of this paper is to explore the differences in the rate of logically inconsistency Health State Valuations between TTO and DCE methodologies. Methods: A representative sample of the Australian general population will be recruited from an online cohort. Of the 243 EQ-5D-3L Health States, a number of Health State sets, comprising of potentially logically inconsistent Health State pairs, will be used for the Valuation. Participants will be asked to value given Health State sets using both TTO and DCE methods. Consequently, the proposed study is not a Health State Valuation exercise, but rather an eValuation of competing methods under controlled circumstances. Logical inconsistency will be assessed based on comparing quantitative Health State Valuations within the TTO and Stated preferences from discrete choices within the DCE. The count of logical inconsistencies will be estimated at an individual level for both approaches and compared. The comparison of the two approaches will identify if there are significant differences between the number of logical inconsistencies produced from DCE and TTO methods.
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Health State Valuation in sri lanka using the eq 5d 3l precursor to qaly estimation in south asia
Value in Health, 2016Co-Authors: Sanjeewa Kularatna, Paul Anthony ScuffhamAbstract:Quality adjusted life years (QALYs) are used commonly as an outcome measure in Health economic eValuations. Health State Valuations are used to determine the country specific utility values which are necessary to derive QALYs. The objective of this study is to derive an algorithm to estimate utility values for the EQ-5D-3L Health States using preferences of Sri Lankan general population.
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Health State Valuation in low and middle income countries a systematic review of the literature
Value in Health, 2013Co-Authors: Sanjeewa Kularatna, Jennifer A Whitty, Newell Walter Johnson, Paul Anthony ScuffhamAbstract:Objective Cost-utility analysis is widely used in high-income countries to inform decisions on efficient Health care resource allocation. Cost-utility analysis uses the quality-adjusted life-year as the outcome measure of Health. High-income countries have undertaken Health State Valuation (HSV) studies to determine country-specific utility weights to facilitate Valuation of Health-related quality of life. Despite an evident need, however, the extent of HSVs in low- and middle-income countries (LMICs) is unclear. Methods The literature was searched systematically by using four databases and additional Web searches to identify HSV studies carried out in LMICs. The Preferred Reporting System for Systematic Reviews and Meta-Analysis (PRISMA) strategy was followed to ensure systematic selection of the articles. Results The review identified 17 HSV studies from LMICs. Twelve studies were undertaken in upper middle-income countries, while lower middle- and low-income countries contributed three and two studies, respectively. There were 7 generic HSV and 10 disease-specific HSV studies. The seven generic HSVs included five EuroQol five-dimensional questionnaire, one six-dimensional Health State short form (derived from short-form 36 Health survey), and one Assessment of Quality of Life Valuations. Time trade-off was the predominant Valuation method used across all studies. Conclusions This review found that Health State Valuations from LMICs are uncommon and utility weights are generally unavailable for these countries to carry out Health economic eValuation. More HSV studies need to be undertaken in LMICs to facilitate efficient resource allocation in their respective Health systems.
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study protocol for valuing eq 5d 3l and eortc 8d Health States in a representative population sample in sri lanka
Health and Quality of Life Outcomes, 2013Co-Authors: Sanjeewa Kularatna, Jennifer A Whitty, Newell Walter Johnson, Paul Anthony ScuffhamAbstract:Background: Economic eValuations to inform decisions about allocation of Health resources are scarce in Low and Middle Income Countries, including in Sri Lanka. This is in part due to a lack of country-specific utility weights, which are necessary to derive appropriate Quality Adjusted Life Years. The EQ-5D-3L, a generic multi-attribute instrument (MAUI), is most widely used to measure and value Health States in high income countries; nevertheless, the sensitivity of generic MAUIs has been criticised in some conditions such as cancer. This article describes a protocol to produce both a generic EQ-5D-3L and cancer specific EORTC-8D utility index in Sri Lanka. Method: EQ-5D-3L and EORTC-8D Health States will be valued using the Time Trade-Off technique, by a representative population sample (n = 780 invited) identified using stratified multi-stage cluster sampling with probability proportionate to size method. Households will be randomly selected within 30 clusters across four districts; one adult (≥18 years) within each household will be selected using the Kish grid method. Data will be collected via face-to-face interview, with a Time Trade-Off board employed as a visual aid. Of the 243 EQ-5D-3L and 81,290 EORTC-8D Health States, 196 and 84 respectively will be directly valued. In EQ-5D-3L, all Health States that combine level 3 on mobility with either level 1 on usual activities or self-care were excluded. Each participant will first complete the EQ-5D-3L, rank and value 14 EQ-5D-3L States (plus the worst Health State and “immediate death”), and then rank and value seven EORTC-8D States (plus “immediate death”). Participant demographic and Health characteristics will be also collected. Regression models will be fitted to estimate utility indices for EQ-5D-3L and EORTC-8D Health States for Sri Lanka. The dependent variable will be the utility value. Different specifications of independent variables will be derived from the ordinal EQ-5D-3L to test for the best-fitting model. Discussion: In Sri Lanka, a LMIC Health State Valuation will have to be carried out using face to face interview instead of online methods. The proposed study will provide the first country-specific Health State Valuations for Sri Lanka, and one of the first Valuations to be completed in a South Asian Country.
Nancy Devlin - One of the best experts on this subject based on the ideXlab platform.
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Review of Valuation Methods of Preference-Based Measures of Health for Economic EValuation in Child and Adolescent Populations: Where are We Now and Where are We Going?
PharmacoEconomics, 2020Co-Authors: Donna Rowen, Nancy Devlin, Oliver Rivero-arias, Julie RatcliffeAbstract:Methods for measuring and valuing Health benefits for economic eValuation and Health technology assessment in adult populations are well developed. In contrast, methods for assessing interventions for child and adolescent populations lack detailed guidelines, particularly regarding the Valuation of Health and quality of life in these age groups. This paper critically examines the methodological considerations involved in the Valuation of child- and adolescent-specific Health-related quality of life by existing preference-based measures. It also describes the methodological choices made in the Valuation of existing generic preference-based measures developed with and/or applied in child and adolescent populations: AHUM, AQoL-6D, CHU9D, EQ-5D-Y, HUI2, HUI3, QWB, 16D and 17D. The approaches used to value existing child- and adolescent-specific generic preference-based measures vary considerably. While the choice of whose preferences and which perspective to use is a matter of normative debate and ultimately for decision by reimbursement agencies and policy makers, greater research around these issues would be informative and would enrich these discussions. Research can also inform the other methodological choices required in the Valuation of child and adolescent Health States. Gaps in research evidence are identified around the impact of the child described in Health State Valuation exercises undertaken by adults, including the possibility of informed preferences; the appropriateness and acceptability of Valuation tasks for adolescents, in particular tasks involving the State ‘dead’; anchoring of adolescent preferences; and the generation and use of combined adult and adolescent preferences.
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Valuing EQ-5D-5L Health States ‘in context’ using a discrete choice experiment
The European Journal of Health Economics, 2018Co-Authors: Amanda Cole, Brendan Mulhern, Koonal Shah, Yan Feng, Nancy DevlinAbstract:Background In Health State Valuation studies, Health States are typically presented as a series of sentences, each describing a Health dimension and severity ‘level’. Differences in the severity levels can be subtle, and confusion about which is ‘worse’ can lead to logically inconsistent Valuation data. A solution could be to mimic the way patients self-report Health, where the ordinal structure of levels is clear. We develop and test the feasibility of presenting EQ-5D-5L Health States in the ‘context’ of the entire EQ-5D-5L descriptive system. Methods An online two-arm discrete choice experiment was conducted in the UK ( n = 993). Respondents were randomly allocated to a control (standard presentation) or ‘context’ arm. Each respondent completed 16 paired comparison tasks and feedback questions about the tasks. Differences across arms were assessed using regression analyses. Results Presenting Health States ‘in context’ can significantly reduce the selection of logically dominated Health States, particularly for labels ‘severe’ and ‘extreme’ (χ^2 = 46.02, p
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Valuing EQ-5D-5L Health States ‘in context’ using a discrete choice experiment
European Journal of Health Economics, 2017Co-Authors: Amanda Cole, Brendan Mulhern, Koonal Shah, Yan Feng, Nancy DevlinAbstract:Background In Health State Valuation studies, Health States are typically presented as a series of sentences, each describing a Health dimension and severity ‘level’. Differences in the severity levels can be subtle, and confusion about which is ‘worse’ can lead to logically inconsistent Valuation data. A solution could be to mimic the way patients self-report Health, where the ordinal structure of levels is clear. We develop and test the feasibility of presenting EQ-5D-5L Health States in the ‘context’ of the entire EQ-5D-5L descriptive system.
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A program of methodological research to arrive at the new international eq-5d-5l Valuation protocol
Value in Health, 2014Co-Authors: Mark Oppe, Nancy Devlin, Paul F M Krabbe, Ben A. Van Hout, Frank De CharroAbstract:Objectives: To describe the research that has been undertaken by the EuroQol Group to improve current methods for Health State Valuation, to summarize the results of an extensive international pilot program, and to outline the key elements of the five-level EuroQol five-dimensional (EQ-5D-5L) questionnaire Valuation protocol, which is the culmination of that work. Methods: To improve on methods of Health State Valuation for the EQ-5D-5L questionnaire, we investigated the performance of different variants of time tradeoff and discrete choice tasks in a multinational setting. We also investigated the effect of three modes of administration on Health State Valuation: group interviews, online self-completion, and faceto-face interviews. Results: The research program provided the basis for the EQ-5D-5L questionnaire Valuation protocol. Two different types of tasks are included to derive preferences: a newly developed composite time trade-off task and a forced-choice paired comparisons discrete choice task. Furthermore, standardized blocked designs for the selection of the States to be valued by participants were created and implemented together with all other elements of the Valuation protocol in a digital aid, the EuroQol Valuation Technology, which was developed in conjunction with the protocol. Conclusions: The EuroQol Group has developed a standard protocol, with accompanying digital aid and interviewer training materials, that can be used to create value sets for the EQ-5D-5L questionnaire. The use of a well-described, consistent protocol across all countries enhances the comparability of value sets between countries, and allows the exploration of the influence of cultural and other factors
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binary choice Health State Valuation and mode of administration head to head comparison of online and capi
Value in Health, 2013Co-Authors: Brendan Mulhern, John Brazier, Louise Longworth, Donna Rowen, Nick Bansback, Nancy Devlin, Aki TsuchiyaAbstract:Background Health State Valuation exercises can be conducted online, but the quality of data generated is unclear.
Katherine Stevens - One of the best experts on this subject based on the ideXlab platform.
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Health State values for the HUI 2 descriptive system: results from a UK survey
2020Co-Authors: Christopher Mccabe, Jennifer Roberts, Katherine Stevens, John BrazierAbstract:This paper reports the results of a study to estimate a statistical Health State Valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble Health State preference data. A sample of 51 Health States were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting Health State Valuations for all 8,000 States defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed Health State values within the Valuation sample and for a separate validation sample of Health States. However, there are concerns over large prediction errors for two Health States in the Valuation sample. These problems must be balanced against concerns over the validity of using the VAS based Health State Valuation data of the original HUI2 Valuation model.
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Health State values for the HUI 2 descriptive system: results from a UK survey
Health Economics, 2020Co-Authors: Christopher Mccabe, Jennifer Roberts, Katherine Stevens, John BrazierAbstract:This paper reports the results of a study to estimate a statistical Health State Valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble Health State preference data. A sample of 51 Health States were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting Health State Valuations for all 8000 States defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed Health State values within the Valuation sample and for a separate validation sample of Health States. However, there are concerns over large prediction errors for two Health States in the Valuation sample. These problems must be balanced against concerns over the validity of using the VAS based Health State Valuation data of the original HUI2 Valuation model. Copyright © 2004 John Wiley & Sons, Ltd.
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Developing adolescent-specific Health State values for economic eValuation: an application of profile case best-worst scaling to the Child Health Utility 9D
PharmacoEconomics, 2012Co-Authors: Julie Ratcliffe, John Brazier, Katherine Stevens, Terry N. Flynn, Frances Terlich, Michael G. SawyerAbstract:This study provides preliminary indications that there may be potentially important and systematic differences in the Valuations attached to identical Health States by adolescents in comparison with adult population groups. The study findings lend support to the potential future application of profile case BWS DCE methods to undertake large-scale Health State Valuation studies directly with young adolescent population samples and provide support for the feasibility and acceptability of a web-based mode of administration for this purpose. Copyright Springer International Publishing AG 2012
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assessing the performance of a new generic measure of Health related quality of life for children and refining it for use in Health State Valuation
Applied Health Economics and Health Policy, 2011Co-Authors: Katherine StevensAbstract:Background Previous research to develop a new generic paediatric Health-related quality of life (HR-QOL) measure generated 11 dimensions of HR-QOL, covering physical, emotional and social functioning. These dimensions and their response scales were developed from interviews with children. Some of these dimensions have alternative wording choices. The measure is intended to be preference based so that it can be used in paediatric economic eValuation.
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Valuing child Health utility 9D Health States with a young adolescent sample
Applied Health Economics and Health Policy, 2011Co-Authors: Julie Ratcliffe, John Brazier, Katherine Stevens, Leah Couzner, Terry Flynn, Michael Sawyer, Leonie BurgessAbstract:QALYs are increasingly being utilized as a Health outcome measure to calculate the benefits of new treatments and interventions within cost-utility analyses for economic eValuation. Cost-utility analyses of adolescent-specific treatment programmes are scant in comparison with those reported upon for adults and tend to incorporate the views of clinicians or adults as the main source of preferences. However, it is not clear that the views of adults are in accordance with those of adolescents on this issue. Hence, the treatments and interventions most highly valued by adults may not correspond with those most highly valued by adolescents. Ordinal methods for Health State Valuation may be more easily understood and interpreted by young adolescent samples than conventional approaches. The availability of young adolescent-specific Health State values for the estimation of QALYs will provide new insights into the types of treatment programmes and Health services that are most highly valued by young adolescents. The first objective of this study was to assess the feasibility of applying best-worst scaling (BWS) discrete-choice experiment (DCE) methods in a young adolescent sample to value Health States defined by the Child Health Utility 9D (CHU9D) instrument, a new generic preference-based measure of Health-related quality of life developed specifically for application in young people. The second objective was to compare BWS DCE questions (where respondents are asked to indicate the best and worst attribute for each of a number of Health States, presented one at a time) with conventional time trade-off (TTO) and standard gamble (SG) questions in terms of ease of understanding and completeness. A feasibility study sample of consenting young adolescent school children (n = 16) aged 11–13 years participated in a face-to-face interview in which they were asked to indicate the best and worst attribute levels from a series of Health States defined by the CHU9D, presented one at a time. Participants were also randomly allocated to receive additional conventional TTO or SG questions and prompted to indicate how difficult they found them to complete. The results indicate that participants were able to readily choose ‘best’ and ‘worst’ dimension levels in each of the CHU9D Health States presented to them and provide justification for their choices. Furthermore, when presented with TTO or SG questions and prompted to make comparisons, participants found the BWS DCE task easier to understand and complete. The results of this feasibility study suggest that BWS DCE methods are potentially more readily understood and interpretable by vulnerable populations (e.g. young adolescents). These findings lend support to the potential application of BWS DCE methods to undertake large-scale Health State Valuation studies directly with young adolescent population samples.
